Segmentation quality assessment (SQA) plays a critical role in the deployment of a medical image based AI system. Users need to be informed/alerted whenever an AI system generates unreliable/incorrect predictions. With the introduction of the Segment Anything Model (SAM), a general foundation segmentation model, new research opportunities emerged in how one can utilize SAM for medical image segmentation. In this paper, we propose a novel SQA method, called SQA-SAM, which exploits SAM to enhance the accuracy of quality assessment for medical image segmentation. When a medical image segmentation model (MedSeg) produces predictions for a test image, we generate visual prompts based on the predictions, and SAM is utilized to generate segmentation maps corresponding to the visual prompts. How well MedSeg's segmentation aligns with SAM's segmentation indicates how well MedSeg's segmentation aligns with the general perception of objectness and image region partition. We develop a score measure for such alignment. In experiments, we find that the generated scores exhibit moderate to strong positive correlation (in Pearson correlation and Spearman correlation) with Dice coefficient scores reflecting the true segmentation quality.

利用Segment Anything Model(SAM)为AI系统提供可靠的分割质量评估，在医学图像分割方面提出了一种新颖的质量评估方法SQA-SAM，将SAM与医学图像分割模型(MedSeg)结合使用，通过对比生成的分割图与视觉提示之间的一致性来评价分割质量。实验结果显示，生成的评分与真实分割质量(Dice系数)呈中度到强正相关。

SQA-SAM: 利用Segment Anything Model对医学图像进行分割质量评估